Group F- Hackathon
“Halve the energy use of new buildings by 2030”
Stakeholders: construction industry, home owners, private landlords, estate agents, housing associations, construction materials manufacturers, skills providers and national and local governments.
What inequalities in building energy usage are there during periods of high and low temperature?
What areas could be supported to achieve the potential energy performance ratings?
During summer or winter, the more extreme temperatures cause people to use additional energy to either cool down or warm up their properties.
Low energy efficiency during such times would mean a lot of wasted energy to achieve that.
Identify regions more affected by the extreme temperatures that also have low energy efficiency/high energy consumption.
For such regions:
What is the make-up of the properties?
Are they mostly old dwellings, are they recently refurbished/insulated etc.?
How much room is there to achieve the potential energy efficiency (does it correlate with dwelling age)?
Once these regions are identified and have enough potential for improvements:
What could be the average costs of improving the energy efficiency of the dwellings within these regions ?
whether they could reasonably be covered by homeowners and public funding needs to be provided?
Energy usage statistics (gas, electric)
Building energy performance ratings
House building statistics
Fuel poverty
Building materials
OS INSPIRE Building Polygons
Dwelling Ages and Prices (LSOA)
Council Tax Band and Build Period by LA, MSOA, LSOA
House Prices for Small Areas (LSOAs)
Land Surface Temperature from MODIS (Moderate Resolution Imaging Spectroradiometer)
Firstly, we can use clustering algorithms in the 3D area-energy efficiency space - are there concentrated zones of low/high efficiency? Can identify the optimal number of clusters from the cluster quality evaluation. Collapsing the clusters onto the 2D area and having the average energy efficiency determining the colour of the cluster would be a good way to answer this question.
Secondly, correlate the energy efficiency and temperature datasets both by rank and product-moment. Do zones of low energy efficiency also experience extreme temperatures?
We then did a KMeans clustering across 2 dimensions of space and the third dimension of energy consumption, and energy efficiency to find spatial locations with similar energy consumption patterns.